Folksonomy-Based Internet Object Profiling and Relation Extracting

Web 2.0 emphasizes user-generated content, usability, and interoperability for end users. As a killer application in web 2.0, Folksonomy is a collaborative tagging system and enables users to annotate Web objects freely and conveniently. In this paper, we propose an approach to build and enrich profiles of Internet objects (users and resources) based on the relations provided by Folksonomy among tags, resources, and users. This approach extracts semantic relations among tags and constructs a tag hierarchy for calculating similarities of Internet objects, so as to let Internet service or content providers perceive meaning of tags as human beings do. We examine the performance of our approach via cross validation on real datasets and show that, with profile enrichment, recommendation performance measure F1 is increased by 10.0417% compared to recommendation without profile enrichment.